TY - JOUR A1 - Freier, Carolin A1 - Bocklet, Tobias A1 - Helten, Anne-Kathrin A1 - Hoffmann, Franziska A1 - Hunger, Marianne A1 - Kovács, László A1 - Richter, Florian A1 - Riedhammer, Korbinian A1 - Schmohl, Tobias A1 - Simon, Claudia T1 - Wie kann videogestütztes Lernen die Erwartungen Studierender und Dozierender erfüllen? JF - Soziale Passagen N2 - Im BMBF-Verbundprojekt HAnS entwickeln und implementieren neun Hochschulen sowie drei hochschulübergreifende Einrichtungen ein intelligentes Hochschul-Assistenz-System als Open-Source-Lösung. Videobasierte Lehrmaterialien werden verschriftlicht und durch eine Indexierung Stichwortsuchen ermöglicht; geplant ist, über einen KI-Tutor automatisiert Übungsaufgaben zu generieren. Studierende sollen so in ihrem Selbststudium digital unterstützt werden. Die technische Entwicklung wird interdisziplinär – auch sozialwissenschaftlich und pädagogisch – begleitet und in einem iterativen Vorgehen evidenzbasiert entsprechend Design-Based-Research angepasst. Wissen und Wertesystem der Anwender*innen, Didaktik, Ethik, Akzeptanz und Datenschutz werden dabei im Entwicklungsprozess einbezogen. N2 - In the Project HAnS nine universities and three cross-institutional initiatives have teamed up on behalf of the German Federal Ministry of Education and Research (BMBF) to design and implement an intelligent university assistance system as open source. The goal is to develop an artificial intelligence (AI) tutor for higher education that transcribes video-based teaching material and enables keyword searches via indexing, but furthermore automatically generates exercises. As a result, students will be digitally assisted in their self-study. According to the design-based research idea, technological development is accompanied by an interdisciplinary approach and is checked and continuously altered throughout the developmental process in terms of user knowledge and values, pedagogical knowledge, ethics, acceptability and data protection. T2 - How can video-assisted learning fulfil the expectations of students and lecturers? UR - https://doi.org/10.1007/s12592-023-00478-0 Y1 - 2023 UR - https://doi.org/10.1007/s12592-023-00478-0 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-44077 SN - 1867-0199 VL - 15 IS - 2 SP - 631 EP - 635 PB - Springer CY - Wiesbaden ER - TY - CHAP A1 - Georges, Munir A1 - Huang, Jonathan A1 - Bocklet, Tobias T1 - Compact Speaker Embedding: lrx-vector T2 - Proceedings Interspeech 2020 UR - https://doi.org/10.21437/Interspeech.2020-2106 KW - speaker recognition KW - x-vector KW - low power Y1 - 2020 UR - https://doi.org/10.21437/Interspeech.2020-2106 SP - 3236 EP - 3240 PB - ISCA CY - Grenoble ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Bocklet, Tobias A1 - Freisinger, Steffen A1 - Frischholz, Lia A1 - Georges, Munir A1 - Glocker, Kevin A1 - Herygers, Aaricia A1 - Peinl, René A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Zakaria, Khabbab ED - Draxler, Christoph T1 - The Hochschul-Assistenz-System HAnS: An ML-Based Learning Experience Platform T2 - Elektronische Sprachsignalverarbeitung 2023: Tagungsband der 34. Konferenz München, 1.-3. März 2023 Y1 - 2023 UR - https://www.essv.de/paper.php?id=1188 SN - 978-3-95908-303-4 SP - 168 EP - 169 PB - TUDpress CY - Dresden ER - TY - CHAP A1 - Georges, Munir A1 - Czarnowski, Krzysztof A1 - Bocklet, Tobias T1 - Ultra-Compact NLU: Neuronal Network Binarization as Regularization T2 - Proceedings Interspeech 2019 UR - https://doi.org/10.21437/Interspeech.2019-2591 KW - ASR KW - NLU KW - SLU KW - binary DNN KW - regularization Y1 - 2019 UR - https://doi.org/10.21437/Interspeech.2019-2591 SP - 809 EP - 813 PB - ISCA CY - Grenoble ER - TY - CHAP A1 - Stemmer, Georg A1 - Georges, Munir A1 - Hofer, Joachim A1 - Rozen, Piotr A1 - Bauer, Josef A1 - Nowicki, Jakub A1 - Bocklet, Tobias A1 - Colett, Hannah R. A1 - Falik, Ohad A1 - Deisher, Michael A1 - Downing, Sylvia J. T1 - Speech Recognition and Understanding on Hardware-Accelerated DSP T2 - Interspeech 2017: Situated interaction UR - https://doi.org/10.21437/Interspeech.2017 KW - speech recognition KW - natural language understanding KW - neural network hardware Y1 - 2017 UR - https://doi.org/10.21437/Interspeech.2017 UR - https://www.isca-speech.org/archive/interspeech_2017/stemmer17_interspeech.html SP - 2036 EP - 2037 PB - ISCA CY - Grenoble ER - TY - CHAP A1 - Freisinger, Steffen A1 - Schneider, Fabian A1 - Herygers, Aaricia A1 - Georges, Munir A1 - Bocklet, Tobias A1 - Riedhammer, Korbinian T1 - Unsupervised Multilingual Topic Segmentation of Video Lectures: What can Hierarchical Labels tell us about the Performance? T2 - Proceedings 9th Workshop on Speech and Language Technology in Education (SLaTE) UR - https://doi.org/10.21437/SLaTE.2023-27 KW - topic segmentation KW - video lectures KW - hierarchical topics KW - multilingual Y1 - 2023 UR - https://doi.org/10.21437/SLaTE.2023-27 SP - 141 EP - 145 PB - International Speech Communication Association (ISCA) CY - Baixas ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Freier, Carolin A1 - Reinold, Luca A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Simon, Claudia A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Bocklet, Tobias ED - Schulz, Sandra ED - Kiesler, Natalie T1 - A Multidisciplinary Approach to AI-based self-motivated Learning and Teaching with Large Language Models T2 - DELFI 2024, Die 22. Fachtagung Bildungstechnologien der Gesellschaft für Informatik e.V. N2 - We present a learning experience platform that uses machine learning methods to support students and lecturers in self-motivated online learning and teaching processes. The platform is being developed as an agile open-source collaborative project supported by multiple universities and partners. The development is guided didactically, reviewed, and scientifically evaluated in several cycles. Transparency, data protection and the copyright compliant use of the system is a central part of the project. The system further employs large language models (LLMs). Due to privacy concerns, we utilize locally hosted LLM instances and explicitly do not rely on available cloud products. Students and lecturers can interact with an LLM-based chatbot in the current prototype. The AI-generated outputs contain cross-references to the current educational video’s context, indicating if sections are based on the lectures context or world knowledge. We present the prototype and results of our qualitative evaluation from the perspective of lecturers and students. UR - https://doi.org/10.18420/delfi2024_11 Y1 - 2024 UR - https://doi.org/10.18420/delfi2024_11 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-51089 SN - 978-3-88579-255-0 SP - 133 EP - 140 PB - Gesellschaft für Informatik CY - Bonn ER - TY - CHAP A1 - Ranzenberger, Thomas A1 - Bocklet, Tobias A1 - Freisinger, Steffen A1 - Georges, Munir A1 - Glocker, Kevin A1 - Herygers, Aaricia A1 - Riedhammer, Korbinian A1 - Schneider, Fabian A1 - Simic, Christopher A1 - Zakaria, Khabbab ED - Baumann, Timo T1 - Extending HAnS: Large Language Models for Question Answering, Summarization, and Topic Segmentation in an ML-based Learning Experience Platform T2 - Elektronische Sprachsignalverarbeitung 2024, Tagungsband der 35. Konferenz, Regensburg, 6.-8. März 2024 UR - https://doi.org/10.35096/othr/pub-7103 KW - hans KW - learning experience platform KW - speech processing KW - natural language processing KW - large language models Y1 - 2024 UR - https://doi.org/10.35096/othr/pub-7103 SN - 978-3-95908-325-6 SP - 219 EP - 224 PB - TUDPress CY - Dresden ER -